code: def test_pretrained_keras_weight(self, keras_name, objax_name):
      # Include top
      model_keras = getattr(tf.keras.applications, keras_name)(
          include_top=True,
          weights="imagenet",
          classes=1000,
          classifier_activation="linear",
      )
      model_objax = load_pretrained_weights_from_keras(
          objax_name, include_top=True, num_classes=1000
      )
      self.check_compatibility(model_keras, model_objax)
      self.check_output_shape(model_objax, 1000)
      del model_keras, model_objax
      # Exclude top
      model_keras = getattr(tf.keras.applications, keras_name)(
          include_top=False, weights="imagenet", pooling=None
      )
      model_objax = load_pretrained_weights_from_keras(
          objax_name, include_top=False, num_classes=10
      )
      self.check_compatibility(
          model_keras, model_objax[:-2], delta=1.001e-05
      )
      self.check_output_shape(model_objax, 10)
  
file_path: objax/tests/resnet_v2.py
imports: import unittest
  from parameterized import parameterized
  from functools import partial
  import numpy
  import tensorflow
  import objax
  from objax.zoo.resnet_v2 import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ResNet200
  from objax.zoo.resnet_v2 import convert_keras_model, load_pretrained_weights_from_keras
label_desc: Use image node_id 3 for calling the TestResNetV2Pretrained obj's underlying member method code with example usage: obj.test_pretrained_keras_weight(keras_name, objax_name) without return types